{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7c9e2048",
   "metadata": {},
   "source": [
    "# Fine-Tuning ChatGPT for Different Data Sets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e8ddc61",
   "metadata": {},
   "source": [
    "# Imports\n",
    "Here we import all the necessary packages and modules for our fine-tuning process.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b410a579",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "e:\\Dropbox\\projects_active\\eth_uzh_hatespeech_llm_finetuning\\src\\gpt_finetuning\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import glob\n",
    "import pprint\n",
    "import time\n",
    "import argparse\n",
    "from ast import literal_eval\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "from tqdm import tqdm\n",
    "\n",
    "import wandb\n",
    "from openai import OpenAI\n",
    "import pandas as pd\n",
    "\n",
    "from utils_2 import (\n",
    "    dataset_has_format_errors,\n",
    "    check_token_statistics_and_cost_estimate,\n",
    "    load_dataset_task_prompt_mappings,\n",
    "    write_jsonl,\n",
    ")\n",
    "from utils_1 import task_num_to_task_name, dataset_num_to_dataset_name, plot_count_and_normalized_confusion_matrix, task_to_display_labels\n",
    "\n",
    "# Get the notebook's full path\n",
    "notebook_path = os.getcwd()\n",
    "\n",
    "# Set module_dir to the notebook's directory\n",
    "module_dir = notebook_path\n",
    "\n",
    "api_key_project = \"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\" #MK's Key\n",
    "#api_key_project = \"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\" #new PRODIGI key\n",
    "\n",
    "client = OpenAI(\n",
    "    api_key=api_key_project,  # This is the default and can be omitted\n",
    ")\n",
    "\n",
    "\n",
    "WANDB_PROJECT_NAME = \"chatgpt_annotations_llm_comparison\"\n",
    "MODEL_NAME = 'gpt-4o-mini-2024-07-18'\n",
    "COMPLETION_RETRIES = 50\n",
    "\n",
    "print(notebook_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "772bcc76",
   "metadata": {},
   "source": [
    "# Utility Functions\n",
    "These functions provide various utilities for tasks such as data loading, token statistics, and other preprocessing steps.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa7e1cb4",
   "metadata": {},
   "source": [
    "## Configuration Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c79142f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configuration Variables\n",
    "# Manually set the variables that were previously handled by argparse\n",
    "\n",
    "# Type of task to run inference on\n",
    "task = 1  # Choices: [1]\n",
    "\n",
    "# Dataset to run inference on\n",
    "dataset = 3  # Choices: [1, 2, 3, 4, 5, 6]\n",
    "\n",
    "# Expert Dataset for Second Evaluation\n",
    "dataset_eval = 'x' # Choises: ['x']\n",
    "\n",
    "# Size of the sample to generate\n",
    "sample_size = '250'  # Choices: ['100','200']\n",
    "\n",
    "# Path to the directory to store the generated samples\n",
    "output_dir = '../../annotations/chatGPT/output_fine_tuning/'\n",
    "\n",
    "# Random seed to use\n",
    "seed = 2019\n",
    "\n",
    "# Path to the directory containing the datasets\n",
    "data_dir = '../../annotations/chatGPT/input_fine_tuning/'\n",
    "\n",
    "# Whether to use the full label\n",
    "not_use_full_labels = False\n",
    "\n",
    "# Path to the dataset-task mappings file\n",
    "dataset_task_mappings_fp = os.path.normpath(os.path.join(module_dir, '../../task_mappings', 'dataset_task_mappings.csv'))\n",
    "\n",
    "# Whether to rewrite the dataframe in OpenAI format\n",
    "rewrite_df_in_openai = True\n",
    "\n",
    "# Number of epochs to train the model\n",
    "n_epochs = 5\n",
    "\n",
    "# Batch Size for learning\n",
    "n_batch = 20\n",
    "\n",
    "# Name of the run\n",
    "run_name = 'finetune_chetGPT_hatespeech_appen'\n",
    "\n",
    "# Temperature to use when generating text\n",
    "temp = 0.0\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71e81ba8",
   "metadata": {},
   "source": [
    "## Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4ef6fa17",
   "metadata": {},
   "outputs": [],
   "source": [
    "def map_label_to_completion(label: str, task_num: int, full_label: bool = True) -> str:\n",
    "    new_label = ''\n",
    "\n",
    "    if task_num == 1:\n",
    "        if full_label:\n",
    "            if str(label) in ['1.0', '1']:\n",
    "                new_label = 'HATE SPEECH'\n",
    "            elif str(label) in ['2.0', '2']:\n",
    "                new_label = 'TOXIC SPEECH'\n",
    "            else:\n",
    "                new_label = 'KEINE HATE SPEECH'\n",
    "            assert new_label in ['HATE SPEECH', 'TOXIC SPEECH', 'KEINE HATE SPEECH']\n",
    "        else:\n",
    "            if str(label) in ['1.0', '1']:\n",
    "                new_label = 'A'\n",
    "            elif str(label) in ['2.0', '2']:\n",
    "                new_label = 'C'\n",
    "            else:\n",
    "                new_label = 'B'\n",
    "            assert new_label in ['A', 'B', 'C']\n",
    "\n",
    "    return new_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "02978fd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_train_and_eval_sets(data_dir: str, dataset_num: int, task_num: int, sample_size: int, dataset_eval:str) \\\n",
    "        -> dict[str, pd.DataFrame]:\n",
    "    datasets = dict()\n",
    "\n",
    "    train_dataset_task_files = glob.glob(os.path.join(data_dir, f'ds_{dataset_num}__task_{task_num}_train_set*.csv'))\n",
    "    eval_set_name = f'ds_{dataset_num}__task_{task_num}_eval_set'\n",
    "    datasets[eval_set_name] = pd.read_csv(os.path.join(data_dir, eval_set_name + '.csv'), encoding='utf-8')\n",
    "\n",
    "    # Load the additional evaluation dataset specified by dataset_eval\n",
    "    second_eval_set_name = f'ds_{dataset_eval}__task_{task_num}_full_eval'\n",
    "    datasets[second_eval_set_name] = pd.read_csv(os.path.join(data_dir, second_eval_set_name + '.csv'), encoding='utf-8')\n",
    "\n",
    "    if sample_size == 'all':\n",
    "        train_dfs_ = {fn.strip('.csv'): pd.read_csv(fn, encoding='utf-8') for fn in train_dataset_task_files}\n",
    "        datasets.update(train_dfs_)\n",
    "    else:\n",
    "        train_df_fn = f'ds_{dataset_num}__task_{task_num}_train_set_{sample_size}'\n",
    "        datasets[train_df_fn] = pd.read_csv(os.path.join(data_dir, train_df_fn + '.csv'), encoding='utf-8')\n",
    "\n",
    "        if train_df_fn not in [os.path.basename(fn).strip('.csv') for fn in train_dataset_task_files]:\n",
    "            raise ValueError(f\"Sample size {sample_size} not found for\"\n",
    "                             f\" dataset {dataset_num} and task {task_num}\")\n",
    "\n",
    "    return datasets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "904e16c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_training_example(system_prompt, user_prompt_format, user_prompt_text, completion):\n",
    "    return {'messages': [\n",
    "        {'role': 'system',\n",
    "         'content': system_prompt},\n",
    "\n",
    "        {'role': 'user',\n",
    "         'content': user_prompt_format.format(text=user_prompt_text)},\n",
    "\n",
    "        {'role': 'assistant',\n",
    "         'content': completion}\n",
    "    ]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bdf692a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def upload_datasets_to_openai(output_dir, not_use_full_labels, rewrite_df_in_openai, datasets):\n",
    "    hatespeech_open_ai_metadata = list()\n",
    "\n",
    "    df_id_metadata = pd.DataFrame() if not os.path.exists('hatespeech_open_ai_metadata.csv') \\\n",
    "        else pd.read_csv('hatespeech_open_ai_metadata.csv')\n",
    "\n",
    "    for df_name, df in datasets.items():\n",
    "        df_jsonl_filename = os.path.join(output_dir, 'temp', df_name + '.jsonl')\n",
    "        write_jsonl(data_list=df['openai_instance_format'].tolist(), filename=df_jsonl_filename)\n",
    "\n",
    "        if not_use_full_labels:\n",
    "            df_name += '_single_letter_labels'\n",
    "\n",
    "        if (not rewrite_df_in_openai and\n",
    "                (len(df_id_metadata) > 0 and df_name in df_id_metadata['df_name'].tolist())):\n",
    "            print(f\"Dataset {df_name} already uploaded to OpenAI\")\n",
    "            continue\n",
    "\n",
    "        print(f\"Uploading {df_name} to OpenAI\")\n",
    "        df_response = client.files.create(\n",
    "            file=open(df_jsonl_filename, \"rb\"), purpose=\"fine-tune\"\n",
    "        )\n",
    "        df_response_dict = df_response.to_dict()\n",
    "        df_file_id = df_response_dict[\"id\"]\n",
    "\n",
    "        # Wait until the file is processed\n",
    "        while True:\n",
    "            file = client.files.retrieve(df_file_id)\n",
    "            file_dict = file.to_dict()\n",
    "            if file_dict[\"status\"] == \"processed\":\n",
    "                break\n",
    "            time.sleep(15)\n",
    "        hatespeech_open_ai_metadata.append({'df_name': df_name, 'file_id': df_file_id})\n",
    "\n",
    "    df_id_metadata = pd.concat([df_id_metadata, pd.DataFrame(hatespeech_open_ai_metadata)])\n",
    "    df_id_metadata.to_csv('hatespeech_open_ai_metadata.csv', index=False)\n",
    "\n",
    "    return df_id_metadata\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ace921b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fine_tune_chat_gpt(evaluation_file_id, training_file_id, model_name, n_epochs):\n",
    "    response = client.fine_tuning.jobs.create(\n",
    "        training_file=training_file_id,\n",
    "        validation_file=evaluation_file_id,\n",
    "        model=\"gpt-4o-mini-2024-07-18\",\n",
    "        suffix=model_name,\n",
    "        hyperparameters={\"n_epochs\": n_epochs,\n",
    "                         \"batch_size\": n_batch}\n",
    "    )\n",
    "\n",
    "    response_dict = response.to_dict()\n",
    "    job_id = response_dict[\"id\"]\n",
    "    print(\"Job ID:\", response_dict[\"id\"])\n",
    "    print(\"Status:\", response_dict[\"status\"])\n",
    "\n",
    "    # Wait until the job is done\n",
    "    while True:\n",
    "        job = client.fine_tuning.jobs.retrieve(job_id)\n",
    "        job_dict = job.to_dict()\n",
    "        if job_dict[\"status\"] == \"succeeded\":\n",
    "            break\n",
    "        elif job_dict[\"status\"] == \"failed\":\n",
    "            raise Exception(\"Training failed: %s\" % job_dict[\"error\"])\n",
    "        time.sleep(30)\n",
    "\n",
    "    return job_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2026f811",
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_and_log_finetuning_event_history(job_id):\n",
    "    response = client.fine_tuning.jobs.list_events(fine_tuning_job_id=job_id)\n",
    "    response_dict = response.to_dict()\n",
    "    events = response_dict[\"data\"]\n",
    "    events.reverse()\n",
    "    for event in events:\n",
    "        print(event[\"message\"])\n",
    "\n",
    "    # Log events\n",
    "    for event in events:\n",
    "        if event['type'] != 'metrics':\n",
    "            continue\n",
    "        data = event['data']\n",
    "        wandb.log(\n",
    "            {\n",
    "                \"train_loss\": data.get(\"train_loss\"),\n",
    "                \"valid_loss\": data.get(\"valid_loss\"),\n",
    "                \"train_mean_token_accuracy\": data.get(\"train_mean_token_accuracy\"),\n",
    "                \"valid_mean_token_accuracy\": data.get(\"valid_mean_token_accuracy\")\n",
    "            },\n",
    "            step=data.get('step', 0)\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebc50a97",
   "metadata": {},
   "source": [
    "# Main Implementation\n",
    "This section contains the main code for fine-tuning the ChatGPT model. It includes setup, data preparation, training, and evaluation steps.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed1b7246",
   "metadata": {},
   "source": [
    "### Connect to WandB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "37564c84",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mkublima\u001b[0m (\u001b[33mdigdemlab\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "wandb version 0.19.7 is available!  To upgrade, please run:\n",
       " $ pip install wandb --upgrade"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "Tracking run with wandb version 0.15.11"
      ],
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       "<IPython.core.display.HTML object>"
      ]
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     "data": {
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       "Run data is saved locally in <code>e:\\Dropbox\\projects_active\\eth_uzh_hatespeech_llm_finetuning\\src\\gpt_finetuning\\wandb\\run-20250228_100231-uwibdf8t</code>"
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     "output_type": "display_data"
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     "data": {
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       "Syncing run <strong><a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/uwibdf8t' target=\"_blank\">finetune_chetGPT_hatespeech_appen</a></strong> to <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
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       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View project at <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison' target=\"_blank\">https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View run at <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/uwibdf8t' target=\"_blank\">https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/uwibdf8t</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<button onClick=\"this.nextSibling.style.display='block';this.style.display='none';\">Display W&B run</button><iframe src='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/uwibdf8t?jupyter=true' style='border:none;width:100%;height:420px;display:none;'></iframe>"
      ],
      "text/plain": [
       "<wandb.sdk.wandb_run.Run at 0x215c11ab7c0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 1/2\n",
    "# Initialize the Weights and Biases run\n",
    "wandb.init(\n",
    "    # set the wandb project where this run will be logged\n",
    "    project=WANDB_PROJECT_NAME,\n",
    "    name=run_name if run_name != '' else f'{MODEL_NAME}_ds_{dataset}_task_{int(task)}'\n",
    "                                                    f'_sample_{sample_size}_epochs_{n_epochs}'\n",
    "                                                    f'_full_label_names_{str(not not_use_full_labels)}'\n",
    "                                                    f'_temp_{temp}',\n",
    "\n",
    "    # track hyperparameters and run metadata\n",
    "    config = {\n",
    "        \"model\": MODEL_NAME,\n",
    "        \"dataset\": dataset_num_to_dataset_name[int(dataset)],\n",
    "        \"task\": task_num_to_task_name[int(task)],\n",
    "        \"epochs\": n_epochs,\n",
    "        \"temp\": temp\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58f1df25",
   "metadata": {},
   "source": [
    "### Load and Process Data Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2454b3ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 3\n",
    "# Load the dataset and filename\n",
    "dataset_idx, dataset_task_mappings = load_dataset_task_prompt_mappings(\n",
    "    dataset_num=dataset, task_num=task, dataset_task_mappings_fp=dataset_task_mappings_fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d35437fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 5\n",
    "# Load the train and eval datasets\n",
    "datasets = load_train_and_eval_sets(\n",
    "    data_dir=data_dir, dataset_num=dataset, task_num=task, sample_size=sample_size, dataset_eval=dataset_eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9f379906",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Artifact prompts>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 4\n",
    "# Get information specific to the dataset\n",
    "label_column = dataset_task_mappings.loc[dataset_idx, \"label_column\"]\n",
    "system_prompt = dataset_task_mappings.loc[dataset_idx, 'zero_shot_prompt']\n",
    "user_prompt_format = dataset_task_mappings.loc[dataset_idx, 'user_prompt']\n",
    "    \n",
    "# Log the system prompt and user_prompt_format as files in wandb\n",
    "prompts_artifact = wandb.Artifact('prompts', type='prompts')\n",
    "with prompts_artifact.new_file('system_prompt.txt', mode='w', encoding='utf-8') as f:\n",
    "    f.write(system_prompt)\n",
    "with prompts_artifact.new_file('user_prompt_format.txt', mode='w', encoding='utf-8') as f:\n",
    "    f.write(user_prompt_format)\n",
    "wandb.run.log_artifact(prompts_artifact)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "47917349",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 6\n",
    "# Generate the training and evaluation examples in the way expected by the Open AI API to finetune chatgpt3.5\n",
    "preprocessed_output_dir = os.path.join(\n",
    "    output_dir, 'preprocessed', 'full_name_labels' if not not_use_full_labels else 'single_letter_labels')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5a9616d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 7\n",
    "for df_name, df in datasets.items():\n",
    "    df['completion_label'] = df[label_column].map(\n",
    "        lambda label: map_label_to_completion(label=label, task_num = task,\n",
    "                                              full_label=not not_use_full_labels)\n",
    "        )\n",
    "    df['openai_instance_format'] = df.apply(\n",
    "        lambda row: create_training_example(\n",
    "            system_prompt=system_prompt, user_prompt_format=user_prompt_format,\n",
    "            user_prompt_text=row['text'],\n",
    "            completion=row['completion_label']\n",
    "        ),\n",
    "        axis=1\n",
    "    )\n",
    "    df['openai_instance_without_completion'] = df['openai_instance_format'].map(lambda x: x['messages'][:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8bcd8c12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Check for errors ds_3__task_1_train_set_250 set: \n",
      "No errors found\n"
     ]
    }
   ],
   "source": [
    "# Section 8\n",
    "print(f'Check for errors {df_name} set: ')\n",
    "assert not dataset_has_format_errors(df['openai_instance_format'].tolist()), f\"Errors found in {df_name}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9b1d7833",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 9\n",
    "df.to_csv(os.path.join(preprocessed_output_dir, df_name + '.csv'), index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24cdc6b4",
   "metadata": {},
   "source": [
    "### Upload Training Dataset to OPEN AI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2fd9b187",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uploading ds_3__task_1_eval_set to OpenAI\n",
      "Uploading ds_x__task_1_full_eval to OpenAI\n",
      "Uploading ds_3__task_1_train_set_250 to OpenAI\n"
     ]
    }
   ],
   "source": [
    "# Section 10\n",
    "# Create jsonl file and upload to OpenAI\n",
    "df_id_metadata =upload_datasets_to_openai(output_dir, not_use_full_labels, rewrite_df_in_openai, datasets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "705758da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 11\n",
    "# delete all files in the temp folder\n",
    "os.system(f\"rm -rf {os.path.join(output_dir, 'temp')}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4519ab5a",
   "metadata": {},
   "source": [
    "### Finetune chatGPT with the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b0ec6e82",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 12\n",
    "# Run training on the train_df samples selected\n",
    "eval_set_name = f'ds_{dataset}__task_{task}_eval_set'\n",
    "eval_df = datasets[eval_set_name]\n",
    "for df_name, df in datasets.items():\n",
    "    if df_name == eval_set_name:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a76b1ab7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finetuning ds_3__task_1_train_set_250\n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Section 13\n",
    "print(f\"Finetuning {df_name}\")\n",
    "print('-' * 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a1036bc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 14\n",
    "# Log in wandb.config the dataset sample size used\n",
    "sample_size = df_name.split('_')[-1]\n",
    "wandb.config['trainset_size'] = sample_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d0860e65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics for the evaluation set: \n",
      "\n",
      "#### Distribution of num_messages_per_example:\n",
      "min / max: 3, 3\n",
      "mean / median: 3.0, 3.0\n",
      "p5 / p95: 3.0, 3.0\n",
      "\n",
      "#### Distribution of num_total_tokens_per_example:\n",
      "min / max: 847, 999\n",
      "mean / median: 879.9213197969543, 869.0\n",
      "p5 / p95: 853.0, 921.4\n",
      "\n",
      "#### Distribution of num_assistant_tokens_per_example:\n",
      "min / max: 4, 6\n",
      "mean / median: 5.324873096446701, 5.0\n",
      "p5 / p95: 4.0, 6.0\n",
      "\n",
      "0 examples may be over the 4096 token limit, they will be truncated during fine-tuning\n",
      "Dataset has ~346689 tokens that will be charged for during training\n",
      "By default, you'll train for 5 epochs on this dataset\n",
      "By default, you'll be charged for ~1733445 tokens\n"
     ]
    }
   ],
   "source": [
    "# Section 15\n",
    "# Check statistics and possible cost for each dataset\n",
    "print('Statistics for the evaluation set: ')\n",
    "eval_tokens = check_token_statistics_and_cost_estimate(\n",
    "    datasets[eval_set_name]['openai_instance_format'].tolist(), target_epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "254bdfe1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics for the training set: \n",
      "\n",
      "#### Distribution of num_messages_per_example:\n",
      "min / max: 3, 3\n",
      "mean / median: 3.0, 3.0\n",
      "p5 / p95: 3.0, 3.0\n",
      "\n",
      "#### Distribution of num_total_tokens_per_example:\n",
      "min / max: 847, 997\n",
      "mean / median: 881.112, 868.5\n",
      "p5 / p95: 853.0, 928.2\n",
      "\n",
      "#### Distribution of num_assistant_tokens_per_example:\n",
      "min / max: 4, 6\n",
      "mean / median: 5.232, 5.0\n",
      "p5 / p95: 4.0, 6.0\n",
      "\n",
      "0 examples may be over the 4096 token limit, they will be truncated during fine-tuning\n",
      "Dataset has ~220278 tokens that will be charged for during training\n",
      "By default, you'll train for 5 epochs on this dataset\n",
      "By default, you'll be charged for ~1101390 tokens\n"
     ]
    }
   ],
   "source": [
    "# Section 16\n",
    "print(f'Statistics for the training set: ')\n",
    "train_tokens = check_token_statistics_and_cost_estimate(df['openai_instance_format'].tolist(),\n",
    "                                                        target_epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "91aed7d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of tokens: 566967\n",
      "\n",
      "#### Estimated cost: 4.54 USD\n"
     ]
    }
   ],
   "source": [
    "# Section 17\n",
    "print(f\"Total number of tokens: {eval_tokens + train_tokens}\\n\")\n",
    "print(f\"#### Estimated cost: {round((eval_tokens + train_tokens) * (0.008 / 1000), 2)} USD\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5a4cb776",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 18\n",
    "# Run the fine-tuning job\n",
    "training_file_id = df_id_metadata.loc[df_id_metadata['df_name'] == df_name, 'file_id'].values[0]\n",
    "evaluation_file_id = df_id_metadata.loc[df_id_metadata['df_name'] == eval_set_name, 'file_id'].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6b0baf56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job ID: ftjob-Ms7qWqWrsQbpHYzepE5kPzFP\n",
      "Status: validating_files\n"
     ]
    }
   ],
   "source": [
    "# Section 19\n",
    "model_name = (df_name.replace('__', '_')\n",
    "              .replace('train_set', 'trn')\n",
    "              .replace('task', 't')\n",
    "              .replace('_single_letter_labels', '_sl'))\n",
    "job_id = fine_tune_chat_gpt(evaluation_file_id, training_file_id, model_name=model_name, n_epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6aff8caf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model ft:gpt-4o-mini-2024-07-18:university-of-zurich-ipz-phd:ds-3-t-1-trn-250:B5qmMUFZ has been successfully fine-tuned\n"
     ]
    }
   ],
   "source": [
    "# Section 20\n",
    "# Print the model name\n",
    "response = client.fine_tuning.jobs.retrieve(job_id)\n",
    "response_dict = response.to_dict()\n",
    "full_model_name = response_dict[\"fine_tuned_model\"]\n",
    "print(f'The model {full_model_name} has been successfully fine-tuned')\n",
    "wandb.config['model_name_openai'] = full_model_name\n",
    "wandb.config['finetuning_jobid'] = job_id\n",
    "wandb.config['training_file_openai_id'] = response_dict['training_file']\n",
    "wandb.config['validation_file_openai_id'] = response_dict['validation_file']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "45b169bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step 48/63: training loss=0.10, validation loss=0.19\n",
      "Step 49/63: training loss=0.10, validation loss=0.13\n",
      "Step 50/63: training loss=0.12, validation loss=0.12\n",
      "Step 51/63: training loss=0.13, validation loss=0.11\n",
      "Step 52/63: training loss=0.11, validation loss=0.14, full validation loss=0.14\n",
      "Step 53/63: training loss=0.12, validation loss=0.18\n",
      "Step 54/63: training loss=0.10, validation loss=0.14\n",
      "Step 55/63: training loss=0.11, validation loss=0.15\n",
      "Step 56/63: training loss=0.12, validation loss=0.12\n",
      "Step 57/63: training loss=0.10, validation loss=0.10\n",
      "Step 58/63: training loss=0.10, validation loss=0.12\n",
      "Step 59/63: training loss=0.11, validation loss=0.09\n",
      "Step 60/63: training loss=0.05, validation loss=0.11\n",
      "Step 61/63: training loss=0.09, validation loss=0.12\n",
      "Step 62/63: training loss=0.07, validation loss=0.19\n",
      "Step 63/63: training loss=0.05, validation loss=0.08, full validation loss=0.12\n",
      "Checkpoint created at step 39\n",
      "Checkpoint created at step 52\n",
      "New fine-tuned model created\n",
      "The job has successfully completed\n"
     ]
    }
   ],
   "source": [
    "# Section 21\n",
    "# Print the events (training history of the model)\n",
    "print_and_log_finetuning_event_history(job_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c579b37",
   "metadata": {},
   "source": [
    "## Evaluate Model on Validation Set with Annotations of the Group from which the Training Data is!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fba537b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "##################################################\n",
      "Getting predictions on the evaluation set\n"
     ]
    }
   ],
   "source": [
    "# Section 22\n",
    "# Evaluate the model on the evaluation set and store the predictions\n",
    "print(\"\\n\" + \"#\" * 50)\n",
    "print(\"Getting predictions on the evaluation set\")\n",
    "predictions = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0a544a5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 394/394 [04:28<00:00,  1.47it/s]\n"
     ]
    }
   ],
   "source": [
    "# Section 23\n",
    "for messages in tqdm(eval_df['openai_instance_without_completion'].tolist()):\n",
    "    # Retry the completion at least COMPLETION_RETRIES times\n",
    "    num_retries = 2\n",
    "    response = None\n",
    "    while num_retries < COMPLETION_RETRIES and response is None:\n",
    "        try:\n",
    "            response = client.chat.completions.create(\n",
    "                model=full_model_name,\n",
    "                messages=messages,\n",
    "                temperature=temp,\n",
    "                n=1\n",
    "            )\n",
    "        except Exception as e:\n",
    "            print('Error getting predictions. Retrying...')\n",
    "            time.sleep(5)\n",
    "            num_retries += 1\n",
    "            if num_retries >= COMPLETION_RETRIES:\n",
    "                print('Maximum amount of retires reached')\n",
    "                raise e\n",
    "    response_dict = response.to_dict()\n",
    "    predictions.append(response_dict['choices'][0]['message']['content'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "337b13ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 24\n",
    "# Add predictions to df\n",
    "eval_df['prediction'] = predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fe242636",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 25\n",
    "# Store output\n",
    "predictions_output_dir = os.path.join(output_dir, 'predictions',\n",
    "                                      f'dataset_{dataset}_task_{task}')\n",
    "os.makedirs(predictions_output_dir, exist_ok=True)\n",
    "datasets[eval_set_name].to_csv(\n",
    "    os.path.join(predictions_output_dir, f\"{model_name}-{run_name}.csv\"),\n",
    "    index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "bcaddde5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 26\n",
    "# Get performance metrics\n",
    "y_true = eval_df['completion_label']\n",
    "y_pred = eval_df['prediction']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3d085eda",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 27\n",
    "label_type = 'full_name' if not not_use_full_labels else 'short_name'\n",
    "display_labels = task_to_display_labels[task][label_type]\n",
    "labels = display_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a1ce1492",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'HATE SPEECH': {'f1-score': 0.5467625899280576,\n",
      "                 'precision': 0.5205479452054794,\n",
      "                 'recall': 0.5757575757575758,\n",
      "                 'support': 66},\n",
      " 'KEINE HATE SPEECH': {'f1-score': 0.6883720930232557,\n",
      "                       'precision': 0.6271186440677966,\n",
      "                       'recall': 0.7628865979381443,\n",
      "                       'support': 194},\n",
      " 'TOXIC SPEECH': {'f1-score': 0.4657534246575342,\n",
      "                  'precision': 0.6,\n",
      "                  'recall': 0.3805970149253731,\n",
      "                  'support': 134},\n",
      " 'accuracy': 0.6015228426395939,\n",
      " 'macro avg': {'f1-score': 0.5669627025362826,\n",
      "               'precision': 0.5825555297577587,\n",
      "               'recall': 0.5730803962070311,\n",
      "               'support': 394},\n",
      " 'weighted avg': {'f1-score': 0.5889377560555152,\n",
      "                  'precision': 0.600043607443437,\n",
      "                  'recall': 0.6015228426395939,\n",
      "                  'support': 394}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Section 28\n",
    "cm_plot, classification_report, metrics = plot_count_and_normalized_confusion_matrix(\n",
    "    y_true, y_pred, display_labels, labels, xticks_rotation='horizontal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "75c64dc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 29\n",
    "# Log metrics\n",
    "for metric_name, metric_value in metrics.items():\n",
    "    wandb.log({metric_name: metric_value})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4aaa4209",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 31\n",
    "# Log the confusion matrix matplotlib figure\n",
    "wandb.log({'confusion_matrix': wandb.Image(cm_plot)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "45aaca05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Artifact classification_report_ds_3_t_1_trn_250>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 32\n",
    "# Log the classification report as an artifact\n",
    "classification_report = (pd.DataFrame({k: v for k, v in classification_report.items() if k != 'accuracy'})\n",
    "                         .transpose().reset_index())\n",
    "\n",
    "wandb.log({'classification_report': wandb.Table(\n",
    "    dataframe=classification_report)})\n",
    "\n",
    "classification_report_artifact = wandb.Artifact(\n",
    "      f'classification_report_{model_name}', type='classification_report')\n",
    "\n",
    "with classification_report_artifact.new_file('classification_report.txt', mode='w') as f:\n",
    "    f.write(pprint.pformat(classification_report))\n",
    "\n",
    "wandb.run.log_artifact(classification_report_artifact)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6db7bbf2",
   "metadata": {},
   "source": [
    "## Evaluation on Second Evaluation Set\n",
    "In this section, we will evaluate the performance of our model on a second evaluation set (`second_eval_set`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8cbb7ff7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['ds_3__task_1_eval_set', 'ds_x__task_1_full_eval', 'ds_3__task_1_train_set_250'])\n"
     ]
    }
   ],
   "source": [
    "print(datasets.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "second_eval_set_name = f'ds_{dataset_eval}__task_{task}_full_eval'\n",
    "full_eval_df = datasets[second_eval_set_name]\n",
    "for df_name, df in datasets.items():\n",
    "    if df_name == second_eval_set_name:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "9cfc903c",
   "metadata": {},
   "outputs": [],
   "source": [
    "full_eval_df = datasets[second_eval_set_name]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "303aeed5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "##################################################\n",
      "Getting predictions on the evaluation set\n"
     ]
    }
   ],
   "source": [
    "# Load the second evaluation set (second_eval_set)\n",
    "# Evaluate the model on the evaluation set and store the predictions\n",
    "print(\"\\n\" + \"#\" * 50)\n",
    "print(\"Getting predictions on the evaluation set\")\n",
    "predictions_2 = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7c218926",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 494/494 [05:38<00:00,  1.46it/s]\n"
     ]
    }
   ],
   "source": [
    "for messages in tqdm(full_eval_df['openai_instance_without_completion'].tolist()):\n",
    "    # Retry the completion at least COMPLETION_RETRIES times\n",
    "    num_retries = 2\n",
    "    response = None\n",
    "    while num_retries < COMPLETION_RETRIES and response is None:\n",
    "        try:\n",
    "            response = client.chat.completions.create(\n",
    "                model=full_model_name,\n",
    "                messages=messages,\n",
    "                temperature=temp,\n",
    "                n=1\n",
    "            )\n",
    "        except Exception as e:\n",
    "            print('Error getting predictions. Retrying...')\n",
    "            time.sleep(5)\n",
    "            num_retries += 1\n",
    "            if num_retries >= COMPLETION_RETRIES:\n",
    "                print('Maximum amount of retires reached')\n",
    "                raise e\n",
    "    response_dict = response.to_dict()\n",
    "    predictions_2.append(response_dict['choices'][0]['message']['content'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "789fce14",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add predictions to df\n",
    "full_eval_df['prediction'] = predictions_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "616c4658",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_output_dir_2 = os.path.join(output_dir, 'predictions_2',\n",
    "                                      f'dataset_{dataset}_task_{task}')\n",
    "os.makedirs(predictions_output_dir_2, exist_ok=True)\n",
    "datasets[second_eval_set_name].to_csv(\n",
    "    os.path.join(predictions_output_dir_2, f\"{model_name}-{run_name}.csv\"),\n",
    "    index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ec8481ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get performance metrics\n",
    "y_true_2 = full_eval_df['completion_label']\n",
    "y_pred_2 = full_eval_df['prediction']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "296deb14",
   "metadata": {},
   "outputs": [],
   "source": [
    "label_type_2 = 'full_name' if not not_use_full_labels else 'short_name'\n",
    "display_labels_2 = task_to_display_labels[task][label_type]\n",
    "labels_2 = display_labels_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "83dbd847",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'HATE SPEECH': {'f1-score': 0.472,\n",
      "                 'precision': 0.5566037735849056,\n",
      "                 'recall': 0.4097222222222222,\n",
      "                 'support': 144},\n",
      " 'KEINE HATE SPEECH': {'f1-score': 0.7299813780260708,\n",
      "                       'precision': 0.697508896797153,\n",
      "                       'recall': 0.765625,\n",
      "                       'support': 256},\n",
      " 'TOXIC SPEECH': {'f1-score': 0.4975124378109453,\n",
      "                  'precision': 0.4672897196261682,\n",
      "                  'recall': 0.5319148936170213,\n",
      "                  'support': 94},\n",
      " 'accuracy': 0.6174089068825911,\n",
      " 'macro avg': {'f1-score': 0.5664979386123387,\n",
      "               'precision': 0.573800796669409,\n",
      "               'recall': 0.5690873719464146,\n",
      "               'support': 494},\n",
      " 'weighted avg': {'f1-score': 0.6105453480342166,\n",
      "                  'precision': 0.6126284506501162,\n",
      "                  'recall': 0.6174089068825911,\n",
      "                  'support': 494}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_plot, classification_report_2, metrics = plot_count_and_normalized_confusion_matrix(\n",
    "    y_true_2, y_pred_2, display_labels_2, labels_2, xticks_rotation='horizontal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "20289ecc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Log metrics\n",
    "for metric_name, metric_value in metrics.items():\n",
    "    wandb.log({metric_name: metric_value})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "09a5abfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Log the confusion matrix matplotlib figure\n",
    "wandb.log({'confusion_matrix_2': wandb.Image(cm_plot)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2399e29a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Artifact classification_report_2_ds_3_t_1_trn_250>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Log the classification report as an artifact\n",
    "classification_report_2 = (pd.DataFrame({k: v for k, v in classification_report_2.items() if k != 'accuracy'})\n",
    "                         .transpose().reset_index())\n",
    "\n",
    "wandb.log({'classification_report_2': wandb.Table(\n",
    "    dataframe=classification_report_2)})\n",
    "\n",
    "classification_report_artifact_2 = wandb.Artifact(\n",
    "      f'classification_report_2_{model_name}', type='classification_report')\n",
    "\n",
    "with classification_report_artifact_2.new_file('classification_report_2.txt', mode='w') as f:\n",
    "    f.write(pprint.pformat(classification_report_2))\n",
    "\n",
    "wandb.run.log_artifact(classification_report_artifact_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b0aa2d8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
       "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
       "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>accuracy</td><td>▁█</td></tr><tr><td>f1</td><td>▁█</td></tr><tr><td>precision</td><td>▁█</td></tr><tr><td>recall</td><td>▁█</td></tr><tr><td>train_loss</td><td>▅▅▇█▆▇▅▆▇▅▅▆▁▄▃▁</td></tr><tr><td>train_mean_token_accuracy</td><td>▄▄▂▁▂▂▃▃▃▅▃▁▇▃▅█</td></tr><tr><td>valid_loss</td><td>█▄▃▃▅▇▅▅▃▂▃▂▃▄█▁</td></tr><tr><td>valid_mean_token_accuracy</td><td>▃▅▇▅▅▄▅▄▅▆█▆▆▅▁▇</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>accuracy</td><td>0.61741</td></tr><tr><td>f1</td><td>0.61741</td></tr><tr><td>precision</td><td>0.61741</td></tr><tr><td>recall</td><td>0.61741</td></tr><tr><td>train_loss</td><td>0.05107</td></tr><tr><td>train_mean_token_accuracy</td><td>1.0</td></tr><tr><td>valid_loss</td><td>0.08159</td></tr><tr><td>valid_mean_token_accuracy</td><td>0.96689</td></tr></table><br/></div></div>"
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View run <strong style=\"color:#cdcd00\">finetune_chetGPT_hatespeech_appen</strong> at: <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/uwibdf8t' target=\"_blank\">https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/uwibdf8t</a><br/>Synced 6 W&B file(s), 4 media file(s), 4 artifact file(s) and 0 other file(s)"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Find logs at: <code>.\\wandb\\run-20250228_100231-uwibdf8t\\logs</code>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
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     "metadata": {},
     "output_type": "display_data"
    }
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   "source": [
    "# Mark the end of the run\n",
    "wandb.finish()"
   ]
  }
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  "kernelspec": {
   "display_name": "chatGPT",
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